Description Details Author(s) References Examples

Variable Clustering with Multiple Latent Components Clustering is based on k-means algorithm. In each step cluster centers are few PCA components, computed for variables in that cluster. The distance is defined by R^2 (obtained by performing least-squares).

The main function of package varclust is
`mlcc.bic`

which allows clustering variables in a data
with unknown number of clusters. Variable partition is computed
with k-means based algorithm. Number of clusters and their dimensions
are computed using BIC criterion.
If the number of clusters is known one might use function `mlcc.reps`

,
which takes number of clusters as a parameter. For `mlcc.reps`

one might
specify as well some initial segmentation for k-means algorithm. This can be useful if
user has some apriori knowledge about clustering.

We also provide function `misclassification`

that computes misclassification
rate between two partitions. This performance measure is
extensively used in image segmentation.

Version: 0.9.21

Piotr Sobczyk, Julie Josse

Maintainer: Piotr Sobczyk [email protected]

Piotr Sobczyk, Malgorzata Bogdan, Julie Josse, *Clustering around latent variables - a technical report*, 2014,
www.im.pwr.edu.pl/~sobczyk/research.html

1 2 3 | ```
sim.data <- data.simulation(n=100, SNR=1, K=5, numb.vars=30, max.dim=2)
mlcc.bic(sim.data$X, numb.clusters=1:5, numb.runs=20)
mlcc.reps(sim.data$X, numb.clusters=5, numb.runs=20)
``` |

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